The approach, called Causal Generative Neural Networks (CGNN), leverages the power of neural networks to learn a generative model of the joint distribution of the observed variables, by minimizing the Maximum Mean Discrepancy between generated and observed data. An approximate learning criterion is ...
We introduce CGNN, a framework to learn functional causal models as generative neural networks. These networks are trained using backpropagation to minimize the maximum mean discrepancy to the observed data. Unlike previous approaches, CGNN leverages both conditional independences and distributional asymme...
run_CGNN_graph.py run_CGNN_graph_hidden_variables.py run_GNN_pairwise_inference.py Tensorflow Implementation of the CGNN Code provided to reproduce the results from the article "Learning Functional Causal Models with Generative Neural Networks" ...
||-GNN[12](Generative Neural Network--PartofCGNN)||-Bivariatefit(Baseline methodofregression)||-Jarfo[20]||-CDS[20]||-RECI[28]||-metrics(Implements the metricsforgraph scoring)||-Precision Recall||-SHD||-SID[29]||-utils|-Settings->SETTINGSclass(hardware settings)|-loss->MMDloss[21,22...
CDTCAM , CGNN , SAM , IGCI , ICA-LiNGAM , ANM , RECI causal-learnICA-LiNGAM , DirectLiNGAM , ANM , PNL , CAM , VARMALiNGAM , longitudinal LiNGAM , MultiGroupDirectLiNGAM , RCD dodiscoverCAM , DAS , NoGAM , SOCRE Functional-based Causal Discovery Methods ...
CausalGenerativeDomainAdaptationNetworksMingmingGong∗†‡KunZhang∗‡BiweiHuang‡ClarkGlymour‡DachengTao§KayhanBatmangh..
[14] (Additive Noise Model) | |- IGCI[15] (Information Geometric Causal Inference) | |- RCC[16] (Randomized Causation Coefficient) | |- NCC[17] (Neural Causation Coefficient) | |- GNN[12] (Generative Neural Network -- Part of CGNN ) | |- Bivariate fit (Baseline method of ...
(Neural Causation Coefficient) | |- GNN[12] (Generative Neural Network -- Part of CGNN ) | |- Bivariate fit (Baseline method of regression) | |- Jarfo[20] | |- CDS[20] | |- RECI[28] | |- metrics (Implements the metrics for graph scoring) | |- Precision Recall | |- SHD |...
The recent advances in the field of deep learning are represented in an approach called CGNN (Causal Generative Neural Networks, [13]). The authors use Generative Neural Networks to model the distribution of one variable given samples from the other variable. As Neural Networks are able to appr...